{
  "id": 17490,
  "url": "https://link.springer.com/article/10.1007/s00146-026-03228-x",
  "title": "Zones of (un)certainty: studying the temporal stabilization of ground truths for medical AI",
  "summary": "Medical data annotation is a critical yet contingent process that (re)configures ambiguous medical decisions and data representations into ground truths for artificial intelligence (AI). Extending critical data, AI, and STS research on uncertainty in data annotation, this article develops the concept of zones of (un)certainty to capture the epistemic time-spaces through which annotators transform uncertainty and dissonance into stabilized ground-truth data for medical AI. Drawing on interviews w",
  "authors": null,
  "category": "research",
  "topics": "healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-07T00:00:00.000Z",
  "fetched_at": "2026-08-08T05:10:34.355Z",
  "source_slug": "x-ai-society",
  "source_name": "AI & Society",
  "source_homepage": "https://link.springer.com/journal/146",
  "ethics_ai_record_url": "https://ethics.ai/record/17490",
  "original_url": "https://link.springer.com/article/10.1007/s00146-026-03228-x",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}